De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data
SourceCell Stem Cell, 19, 2, (2016), pp. 266-77
Article / Letter to editor
Display more detailsDisplay less details
Cell Stem Cell
SubjectRadboudumc 2: Cancer development and immune defence RIMLS: Radboud Institute for Molecular Life Sciences
Adult mitotic tissues like the intestine, skin, and blood undergo constant turnover throughout the life of an organism. Knowing the identity of the stem cell is crucial to understanding tissue homeostasis and its aberrations upon disease. Here we present a computational method for the derivation of a lineage tree from single-cell transcriptome data. By exploiting the tree topology and the transcriptome composition, we establish StemID, an algorithm for identifying stem cells among all detectable cell types within a population. We demonstrate that StemID recovers two known adult stem cell populations, Lgr5+ cells in the small intestine and hematopoietic stem cells in the bone marrow. We apply StemID to predict candidate multipotent cell populations in the human pancreas, a tissue with largely uncharacterized turnover dynamics. We hope that StemID will accelerate the search for novel stem cells by providing concrete markers for biological follow-up and validation.
This item appears in the following Collection(s)
- Academic publications 
- Electronic publications 
- Faculty of Medical Sciences 
- Open Access publications 
Upload full text
Use your RU credentials (u/z-number and password) to log in with SURFconext to upload a file for processing by the repository team.